Determining whether a patient/individual is awake or asleep is an essential first step in the analysis of sleep records obtained during investigation of sleep disorders. In some cases such investigations require only knowledge of whether the patient was awake or asleep. An example would be home monitoring for the diagnosis of sleep apnea. Here, if a patient does not show evidence of sleep apnea (e.g. dips in oxygen saturation, interrupted snoring) a diagnostic dilemma arises in that one does not know whether the negative study was because the patient did not sleep. In other cases, it is necessary to have a more comprehensive description of sleep, such as amount of time spent in each of the different sleep stages, which reflect the type rapid edge movement (REM vs. non-REM) and depth (stages N1, N2, N3) of sleep. This information is needed to evaluate the quality of sleep and is particularly useful in cases of excessive somnolence and insomnia. In the latter cases, distinguishing a sleep state from an awake state is a first step towards determining which stage the patient is in. Typically, once it is clear that the patient is asleep, decisions as to what sleep stage the patient is in is based on the presence of specific features in an electroencephalogram (EEG), Eye movements (EOG), intensity of chin muscle activity (chin EMG), among other findings.
Apart from analysis of formal sleep records, it is of considerable importance to be able to determine the level of vigilance in situations that require a high level of alertness such as during driving long distances, operating heavy machinery or equipment of critical nature such as air-traffic control. It is well known that decreased alertness, for example, as a result of boredom, alcohol, drugs, or sleep deprivation, are responsible for numerous driving and occupational accidents. There are different levels to what is considered as wakefulness. These range from fully alert to drowsy to having periods (a few seconds) of micro-sleep. Cognitive and motor performance is impaired as level vigilance decreases even if the subject is still technically awake. To my knowledge, there are currently no methods that identify different levels of wakefulness.
The present invention deals with a method for developing a continuous quantitative scale that describes the level of vigilance/consciousness across the whole spectrum from full alertness to the deepest sleep. When embedded in appropriate equipment this method can be used to a) evaluate the level of vigilance in situations requiring alertness, b) determine whether a subject is awake or asleep, c) determine the quality of sleep in sleep studies and, d) as an initial step in detailed sleep scoring with the subsequent steps relying on identification of the additional features using any of well described approaches in prior art. The current method does not cover steps to classify sleep into its various conventional stages. Rather, the current process generates a value (Probability of being awake (PW); Odds Ratio Product (ORP)), which reflects the probability of any given section of the EEG record falling in a period that would be staged as awake by experienced scorers or by validated automatic scoring systems. I have established the presence of a clear negative correlation between this value (PW, ORP) and depth of sleep as measured by conventional visual criteria. As such, P PW/ORP can be used as a continuous scale that describes the quality of wakefulness or sleep in certain sections of the record or as a lumped average for the whole night. Every sleep technologist recognizes that within any given conventional sleep stage there is a continuum of sleep quality. For example, an EEG pattern that is now classified as stage N1 according to conventional criteria could be very close to an awake pattern on one end of the spectrum or very close to the deeper stage 2 on the other end. Likewise, there is a huge range of patterns in what is now classified as an awake state, ranging from full wakefulness to quite wakefulness, to wakefulness interrupted by mini-sleep periods, and so on. The use of this index (PW ORP) allows an expression of the quality of sleep on a continuous scale regardless of the conventional classification. It also can be used to reflect the overall quality of sleep in one number. This is much easier to understand and interpret than the conventional histogram of the different stages vs. time (the Hypnogram).
The current accepted practice for scoring sleep records is manual scoring by expert technologists. This is time consuming, and by extension, quite expensive. Manual scoring is also highly subjective with different experts producing different results. As indicated above, the EEG pattern in many of the epochs (usually 30 seconds in length) are on the border between two stages (e.g. awake vs. N1). Some may score these epochs one way while others may score it another way. Also, there are large differences in how experts interpret the guidelines, which are often vague. Manual scoring is also an extremely tedious task and is often associated with gross errors related to inattention. Automation, accordingly, has many potential advantages, if it can be shown to be accurate.
Manual scoring of sleep relies primarily on visual appreciation of the different EEG patterns. There have been many attempts at automating EEG scoring but the results have not been up to what is required for acceptance. Virtually all automated methods rely on frequency analysis of the EEG. This analysis produces the power in different frequencies. The relevant frequency content of the EEG is 0.3 to 40 Hz. Any EEG pattern can be accurately described by the power spectrum of the EEG, namely the power in each of the relevant frequencies. Many previous approaches have been described that exploit the power spectrum of the EEG to arrive at sleep stages. These approaches typically use various complex signal analysis models. The problem is that there is a huge number of frequency spectra that could be called awake and another huge number of patterns that could fall in what the eye perceives as sleep, and so many patterns that could be called either by eye. A high power in the beta range (>14 Hz) may be present in full wakefulness or in the deepest sleep. Likewise, a high alpha power (7 to 14) could be present in wakefulness or in any of the other sleep stages. Thus, the interpretation of power in a given frequency must take into account the power in other relevant frequencies. Yet, as indicated earlier, the various combinations of powers that can be encountered during wakefulness or sleep are enormous and do not lend themselves to a unitary quantitative model. Hence in this invention we use an empiric approach by assigning codes to thousands of EEG frequency patterns and simply determining how often each code is found in epochs that expert scorers score as awake or asleep. Once a reference resource is established (probability of each code to be scored awake or asleep), scoring of un-scored files simply entails determining the spectral code of selected EEG intervals and determining the probability of Sleep/Wake state by use of the reference resource.